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🧩 Interfacing with different LLMs-chains, vectorstore databases, and autonomous agents.

Controlled HALlucination-Evaluation (CHALE) Question-Answering Dataset

Mining and Analyzing Questions from Research Paper TItles

Interactive Reading Environment in Web-Based Virtual Reality (thesis project)

Question-Answer Generation Control for English, using the T5 model and Pytorch lightning. Training, inference and evaluation scripts included.

Techno Smarter QA is a complete Q&A question-answer solution that helps you build your own QA forum website.It's complete Q&A question-answer website script.You can easily ceate your knowldege & opinion sharing platform with it.This Q&A system is fully responsive , lightweight and secure. Let's create your own Q&A question-answer website.

[WEB] mock up Stackoverflow features including Post a qustion, post an answer, upvoting and downvoting question and answer, and watch feature that track a specific question genre. Developed using Vuejs, Express, and Mongodb

💬 A simple & responsive Q&A platform built with ⚙️ Core PHP and 🗃️ MySQL — ask questions, post answers, and explore categorized topics like Stack Overflow.

Question and answer (Q&A) site

A context-aware Retrieval-Augmented Generation (RAG) chatbot built with LangChain v1.0+, OpenAI, FAISS, and Streamlit that delivers accurate, grounded answers with conversational memory.

Submission for HACKNYU. UnStuck is a web community aimed for students through a Q&A system. Created under 4 hours

This is the avishkaarak-ekta-hindi model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering.

Frequency Asked Question module from ommu platform

Detective mini-game: Find the link between 3 randomly sampled Wikipedia page by asking questions.

This is a question answering bot. This website uses OpenAI to achieve this functionality!

Question to me and Response to one

This is a "Question and Answer" application built using IBM watsonx.ai flows engine. The project leverages a vector database to enhance the Large Language Model's (LLM) context awareness with a set of documents, specifically watsonxdocs.

Question Answering (QA) system with RAG supported designed specifically for the Malaysian language.